Chao Shang

Writing tool, resource and execution policies into a task agent's prompt does not reliably prevent violations. AgentPolicyForge uses executable environments to build policy benchmarks and iteratively train policy guards, yielding PolicyGym, which spans ten policy families with 698 scenarios for training-data generation and 254 separate held-out scenarios. On PolicyGym, the final guard-based configuration achieves a 40–44% relative reduction in attack success rate and fewer benign rejections than policy prompting alone.

Writing tool, resource and execution policies into a task agent's prompt does not reliably prevent violations. AgentPolicyForge uses executable environments to build policy benchmarks and iteratively train policy guards, yielding PolicyGym, which spans ten policy families with 698 scenarios for training-data generation and 254 separate held-out scenarios. On PolicyGym, the final guard-based configuration achieves a 40–44% relative reduction in attack success rate and fewer benign rejections than policy prompting alone.